SCH: Heterogenous, dynamic synthetic data: From algorithms to clinical applications

获得健康数据是开发和验证临床应用新AI方法的主要障碍 因为健康数据受到严格的隐私法保护。当前数据中的一个重大障碍 供应的一个重要特点是，现有的访问和去识别健康数据的方法越来越多地被 他们的有效性受到挑战，共同的看法是， 去识别任何健康数据集，并仍然保留用于研究目的的实用性。 合成数据是解决这一难题的一个很有前途的概念，通过协调数据创新与 数据隐私。合成数据的目标是创建一个尽可能真实的数据集， 现有数据-保持原始数据集的统计属性，但没有风险 泄露敏感信息的危险虽然合成数据在医疗保健领域并不新鲜，但到目前为止，它仅限于 简单、单一模态、静态数据集，这严重影响了其影响。 这项跨学科研究工作的目的是开发一个算法框架， 忠实和隐私保护的异构，动态合成数据集的生成，以提高 开发临床决策支持应用程序。 在美国，每年有相当多的美国人受到重大疾病的影响，估计有400万 每年有50万人入院和死亡。相当大比例的病人患有呼吸衰竭 需要插管为了提高算法在临床应用中的效用，如在ICU中， 需要解决使用复杂数据的障碍。因此，ICU是一个典型的环境， 高质量的合成数据将极大地有助于突破这一数据瓶颈， 尊重健康数据隐私法。然而，确定数据来测试和验证算法是非常困难的。 很难获得。因此，本项目建议使用一种严重呼吸（肺）衰竭，急性 呼吸窘迫综合征（ARDS）研究使用合成数据开发人工 智能算法ARDS患者的发病率和死亡率很高， 长期机械通气高医院相关费用，以及长期的身体和心理 功能障碍使用ARDS作为一个原型模型来指导这项研究工作将确保成功 从理论到临床实践。 相关性（参见说明）： 该项目的结果将在推进健康领域的人工智能研究方面发挥关键作用，特别是在以下领域 高风险、高成本的护理，如急诊室、手术室和ICU。在特定的层面上， 该项目将改善急性呼吸窘迫综合症的检测和治疗。在更广泛的 这一努力将有助于提高医疗保健的成本效益，同时改善患者治疗 结果。

Gaining access to health data is the major barrier in developing and validating new AI methods for clinical applications, since health data are protected by strict privacy laws. A significant obstacle in current data provisioning is that existing methods to access and deidentifying health data are increasingly being challenged for their effectiveness, with a common perception that it is generally impossible to fully deidentify any health data set and still retain utility for research purposes. Synthetic data is a promising concept for solving this conundrum, by reconciling data innovation with data privacy. The goal of synthetic data is to create an as-realistic-as-possible dataset generated from existing data - one that maintains the statistical properties of the original dataset, but does so without risk of exposing sensitive information. While synthetic data is not new in health care, so far it was limited to simple, single-modality, static datasets, which severely affected its impact. The aim of this interdisciplinary research effort is the development of an algorithmic framework for the faithful and privacy-preserving generation of heterogeneous, dynamic synthetic datasets to boost the development of clinical decision support applications. In the US, critical illness effects a significant number of Americans per year with an estimated 4 million admission and 500,000 deaths per year. A sizable proportion of the patients suffer respiratory failure requiring intubation. To increase the utility of algorithms in clinical applications, like in the ICU, strategies are needed to address barriers to use of complex data. Thus, the ICU is a prototypical setting where high-quality synthetic data would be tremendously helpful to break through this data bottleneck, while respecting health data privacy laws. However, ascertaining data to test and validate the algorithms is difficult to obtain. As such, this project proposes to use a type of severe respiratory (lung) failure, acute respiratory distress syndrome (ARDS) to study the use of synthetic data for the development of artificial intelligence-based algorithms. Patients with ARDS experience substantial morbidity and mortality, prolonged mechanical ventilation high hospital-associated costs, and long-term physical and psychological dysfunction. Using ARDS as an archetypical model to guide this research effort will a ensure successful transition from theory to clinical practice. RELEVANCE (See instructions): The results of this project will play a key role in advancing AI research in health, especially in areas of high-risk, high-cost care such as the emergency department, operating room, and ICU. On a specific level, the project will improve detection and treatment of the acute respiratory distress syndrome. On a broader level, this effort will contribute to more cost-efficient health care while enabling improved patient treatment outcomes.